Determining acceptable responses for navigating a vehicle that accounts for external conditions of the vehicle

ABSTRACT

Sensor data indicating a substantially 360 degree surrounding of a vehicle is received via a processor included in the vehicle. The sensor data is collected using multiple sensors included in the vehicle. Additionally, an acceptable response time range for a driver of the vehicle to perform an action with the vehicle is obtained, via the processor, based on the sensor data. Additionally, an actual response time for the driver to perform the action is determined, via the processor, based on CAN data collected from a CAN bus included in the vehicle, and not based on the sensor data. Additionally, a determination is made, via the processor, that the actual response time is not within the acceptable response time range. Additionally, a remedial action is caused, via the processor, to be performed in response to the determining that the actual response time is not within the acceptable response time range.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/735,823, filed on May 3, 2022, and entitled “DETERMINING ACCEPTABLERESPONSES FOR NAVIGATING A VEHICLE THAT ACCOUNTS FOR EXTERNAL CONDITIONSOF THE VEHICLE”, which is incorporated herein by reference.

FIELD

One or more embodiments are related to determining one or moreacceptable responses for navigating a vehicle that accounts for externalconditions of the vehicle.

BACKGROUND

Vehicles and/or drivers of vehicles can be monitored to ensure thatdesirable behavior is exhibited and/or undesirable behavior is notexhibited. Some known systems use sensor data indicating only aparticular surrounding region of a vehicle, such as an area in front ofthe vehicle (and not, for example, the areas to the back, left, andright of the vehicle). Relying on sensor data indicating only aparticular surrounding region, however, can cause an undesirable numberof false positive and/or false negative assumptions to be made aboutvehicle and/or driver behavior. Additionally, some known systems relysolely on sensor data when monitoring behavior, such as images and/orvideo captured by a camera. Relying solely on such sensor data, however,may be too slow and inefficient for the high-speed, quick reactsituations that vehicles and/or drivers of the vehicles often encounterwhile driving. Additionally, some known systems do not sense and/orincorporate how other vehicles are acting and/or how a scenario isevolving when considering if monitored behavior is desirable and/orundesirable.

SUMMARY

In one or more embodiments, an apparatus includes a plurality ofsensors, a memory, and a processor operatively coupled to the pluralityof sensors and the memory. Sensor data indicating a substantially 360degree surrounding of a vehicle is received via the processor, theprocessor included in the vehicle. The sensor data is collected usingthe plurality of sensors, the plurality of sensors included in thevehicle. Additionally, an acceptable response time range for a driver ofthe vehicle to perform an action with the vehicle is obtained, via theprocessor, based on the sensor data. Additionally, an actual responsetime for the driver to perform the action is determined, via theprocessor, based on control area network (CAN) data collected from a CANbus included in the vehicle, and not based on the sensor data.Additionally, a determination is made, via the processor, that theactual response time is not within the acceptable response time range.Additionally, a remedial action is caused, via the processor, to beperformed in response to the determining that the actual response timeis not within the acceptable response time range.

In one or more embodiments, a non-transitory processor-readable mediumstores code representing instructions to be executed by one or moreprocessors. The instructions include code to cause the one or moreprocessors to receive a first set of sensor data indicating asurrounding of a vehicle at a first environment, the one or moreprocessors included in the vehicle. The first set of sensor data iscollected using a plurality of sensors included in the vehicle.Additionally, the instructions further include code to cause the one ormore processors to determine a first set of acceptable responses for adriver of the vehicle based on the first set of sensor data.Additionally, the instructions further include code to cause the one ormore processors to determine a first actual response of the driver basedon a first set of control area network (CAN) data collected from a CANbus included in the vehicle. Additionally, the instructions furtherinclude code to cause the one or more processors to determine that thefirst actual response is not included in the first set of acceptableresponses. Additionally, the instructions further include code to causethe one or more processors to cause a remedial action to be performed inresponse to the determining that the first actual response is notincluded in the first set of acceptable responses. Additionally, theinstructions further include code to cause the one or more processors toreceive a second set of sensor data different than the first set ofsensor data and indicating a surrounding of the vehicle at a secondenvironment different than the first environment. The second set ofsensor data is collected using the plurality of sensors included in thevehicle. Additionally, the instructions further include code to causethe one or more processors to determined a second set of acceptableresponses different than the first set of acceptable responses for thedriver is determined based on the second set of sensor data.Additionally, the instructions further include code to cause the one ormore processors to determine a second actual response of the driverbased on a second set of CAN data collected from the CAN bus included inthe vehicle. Additionally, the instructions further include code tocause the one or more processors to determine that the second actualresponse is included in the second set of acceptable responses.

In one or more embodiments, a method includes receiving a first trainingdataset. The first training dataset can include (1) a first set ofsensor data indicating a surrounding of a first vehicle, and (2) arepresentation of a response of a driver of the first vehicle to performan action based on a set of control area network (CAN) data collectedfrom a CAN bus included in the first vehicle. Additionally, the methodfurther includes receiving a second training dataset. The secondtraining data can include (1) a second set of sensor data indicating asurrounding of a second vehicle, and (2) a representation of a responseof a driver of the second vehicle to perform the action based on a setof CAN data collected from a CAN bus included in the second vehicle.Additionally, the method further includes training an anomaly detectionmodel, using the first training dataset and the second training dataset,to produce a trained anomaly detection model. Additionally, the methodfurther includes receiving (1) a third set of sensor data indicating asurrounding of a third vehicle, and (2) a representation of a responseof a driver of the third vehicle to perform the action based on a set ofCAN data collected from a CAN bus included in the third vehicle.Additionally, the method further includes determining that the responseof the driver of the third vehicle is anomalous using the trainedanomaly detection model, the third set of sensor data, and therepresentation of the response of the driver of the third vehicle.Additionally, the method further includes causing a remedial action tobe performed in response to determining that the response of the driverof the third vehicle anomalous.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of a system for monitoring driver behavior,according to an embodiment

FIG. 2 shows a flowchart of a method for causing a remedial action to beperformed in response to determining that an actual response time is notwithin an acceptable response time range for a given scenario, accordingto an embodiment.

FIG. 3 shows a flowchart of a method for comparing, at each environmentfrom multiple different environments, an acceptable response and actualresponse for that environment, according to an embodiment.

FIG. 4 shows a flowchart of a method for determining that a response ofa driver is anomalous, according to an embodiment.

DETAILED DESCRIPTION

Driver behavior can be monitored based on a surrounding environment of avehicle, as well as in-vehicle actions performed by a driver of thevehicle. The surrounding environment of the vehicle can be analyzed todetermine a set of responses that are acceptable for the driver toperform and/or a set of responses that are not acceptable for the driverto perform given the surrounding environment. Thereafter, the in-vehicleactions of the driver can be inferred using, for example, control areanetwork (CAN) bus data. Thereafter, the in-vehicle actions can becompared to the set of responses that are acceptable for the driver toperform and/or the set of responses that are not acceptable for thedriver to perform at the surrounding environment to determine if aremedial action should occur, such as recommending the driver to receivefurther training or adjusting a mode of operation of the vehicle.

Furthermore, in some implementations, data from a fleet includingmultiple vehicles can be received and/or analyzed to generate and/orupdate a software model that can monitor driver behavior for the fleet.In some instances, the software model can identify anomalous drivingbehavior of one or more drivers associated with the fleet, undesirabledriving behaviors for one or more drivers associated with the fleet, oneor more drivers associated with the fleet that could use additionaltraining, and/or the like.

In some instances, techniques described herein increase overall safetyat roads. Drivers that are exhibiting risky behavior can be identified,and mitigating actions can take place. Increased safety at roads canhave multiple advantages, such as reducing risk of traffic accidents andsaving lives.

Furthermore, in some implementations, techniques described herein canconsider CAN bus data from a vehicle instead of and/or in addition tosensor data. CAN bus data can allow some actions of the vehicle and/ordriver to be determined faster than sensor data. As such, the actions ofthe vehicle and/or driver can be determined and/or compared to the setof responses that are acceptable for the driver to perform and/or theset of responses that are not acceptable for the driver to perform in afaster manner (e.g., ˜200-300 milliseconds earlier).

Furthermore, in some instances, the techniques described herein can beapplied to heavy vehicles, such as semi trucks. Due to their relativelylarger weight and size, heavy vehicles and/or drivers of heavy vehiclesexhibiting undesirable behavior can be riskier than, for example, lighttrucks and/or drivers of light trucks. As such, ensuring that heavyvehicles and/or drivers of heavy vehicles are not exhibiting undesirablebehaviors can be particularly desirable (e.g., compared to vehicles thatare lighter than heavy vehicles).

FIG. 1 shows a block diagram of a system for monitoring driver behavior,according to an embodiment. FIG. 1 includes a vehicle 100 operativelycoupled to a compute device 170 via a network 150.

The network 150 can be any suitable communications network fortransferring data, operating over public and/or private networks. Forexample, the network 150 can include a private network, a VirtualPrivate Network (VPN), a Multiprotocol Label Switching (MPLS) circuit,the Internet, an intranet, a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), a worldwideinteroperability for microwave access network (WiMAX®), an optical fiber(or fiber optic)-based network, a Bluetooth® network, a virtual network,and/or any combination thereof. In some instances, the network 150 canbe a wireless network such as, for example, a Wi-Fi or wireless localarea network (“WLAN”), a wireless wide area network (“WWAN”), and/or acellular network. In other instances, the network 150 can be a wirednetwork such as, for example, an Ethernet network, a digitalsubscription line (“DSL”) network, a broadband network, and/or afiber-optic network. In some instances, the network can use ApplicationProgramming Interfaces (APIs) and/or data interchange formats, (e.g.,Representational State Transfer (REST), JavaScript Object Notation(JSON), Extensible Markup Language (XML), Simple Object Access Protocol(SOAP), and/or Java Message Service (JMS). The communications sent viathe network 150 can be encrypted or unencrypted. In some instances, thecommunication network 150 can include multiple networks or subnetworksoperatively coupled to one another by, for example, network bridges,routers, switches, gateways and/or the like (not shown).

The vehicle 100 includes a processor 102, sensors 106, and memory 108,each operatively coupled to one another (e.g., via a system bus). Insome implementations, the processor 102, sensors 106, and/or memory 108are operatively coupled to one another via a control area network (CAN)bus included in the vehicle 100.

The vehicle 100 can operate in a fully autonomous mode (and not asemi-autonomous or manual mode), a semi-autonomous mode (and not a fullyautonomous or manual mode), a manual mode (and not a fully autonomous orsemi-autonomous mode), or a combination thereof. In someimplementations, the vehicle 100 can be a medium truck, heavy truck,very heavy truck, semi-truck, greater than 14,000 pounds, greater than26,000 pounds, greater than 70,000 pounds, or greater than 80,000pounds. In some implementations, the vehicle 100 is a tractor attachedto a trailer.

The processor 102 can be, for example, a hardware based integratedcircuit (IC) or any other suitable processing device configured to runand/or execute a set of instructions or code. For example, the processor102 can be a general-purpose processor, a central processing unit (CPU),an accelerated processing unit (APU), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA), a programmablelogic array (PLA), a complex programmable logic device (CPLD), aprogrammable logic controller (PLC) and/or the like. In someimplementations, the processor 102 can be configured to run any of themethods and/or portions of methods discussed herein.

The sensors 106 can include one or more sensors for collecting sensordata (e.g., sensor data 112, discussed below). The sensors 106 can beused to observe and gather any information that could be useful forperforming the techniques discussed herein, such as informationassociated with a surrounding environment of the vehicle 100 (e.g.,nearby obstacles and their attributes, lane markers and theirattributes, weather, etc.), information associated with the vehicle 100itself (e.g., speed, acceleration rate, location, lane position, etc.),and/or information about a driver of the vehicle 100 (e.g., posture,facial expression, heart rate, speech, movements, mental state, etc.).The sensors 106 can include, for example, at least one of a camera, aradar, a lidar, a microphone, an inertial measurement unit (IMU), or agyroscope. In some implementations, the sensors 106 include multiplecameras, multiple radars, and multiple lidars. In some implementations,at least a portion of the sensors 106 are located at the vehicle 100such that a substantially 360 degree surrounding of a vehicle 100 can bedetermined using data collected by the sensors 106. In someimplementations, a substantially 360 degree surrounding of the vehicle100 includes a region in front of the vehicle 100, a region to the leftof the vehicle 100, a region to the right of the vehicle 100, and aregion behind the vehicle 100. In some implementations, a substantially360 degree surrounding of the vehicle 100 includes at least 300 degreesaround the vehicle 100, at least 325 degrees around the vehicle 100, atleast 350 degrees, or around the vehicle 100, at least 355 degreesaround the vehicle 100, at least 358 degrees around the vehicle 100, atleast 359 degrees around the vehicle 100, etc. In some implementations,one or more of the sensors 106 are located at the vehicle 100 such thata facial expression and/or body posture of a driver of the vehicle 100can be captured. For example, a camera can be located within a cabin ofvehicle 100 and positioned to capture a facial expression and/or bodyposture of a driver of the vehicle 100.

The memory 108 can be, for example, a random-access memory (RAM), amemory buffer, a hard drive, a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM), and/or the like. In someinstances, the memory 108 can store, for example, one or more softwareprograms and/or code that can include instructions to cause theprocessor 102 to perform one or more processes, functions, and/or thelike. In some embodiments, the memory 108 can include extendable storageunits that can be added and used incrementally. In some implementations,the memory 108 can be a portable memory (e.g., a flash drive, a portablehard disk, and/or the like) that can be operatively coupled to theprocessor 102. In some instances, the memory 108 can be remotelyoperatively coupled with a compute device (not shown); for example, aremote database device can serve as a memory and be operatively coupledto the compute device.

The memory 108 can include (e.g., store) sensor data 112. The sensordata 112 can be collected by the sensors 106. The sensor data 112 caninclude for example information about the vehicle's 100 surroundingenvironment, such as attributes (e.g., type, size, speed, position,relative distance, acceleration, etc.) of nearby obstacles (e.g.,vehicles, buildings, pedestrians, lane dividers, sidewalks, etc.),attributes (e.g., location and size) of shoulder areas, attributes(e.g., shape and grade) of a road, weather conditions, and/or the like.The sensor data 112 can also include information about the vehicle 100itself, such as the vehicle's 100 speed, location, tire pressure,internal temperature, audio being played, lights that are on or off,windshield wiper settings, window settings, tractor and trailer state,and/or the like; additionally or alternatively, such information aboutthe vehicle 100 itself can be indicated by the CAN data 116, as will bediscussed below. The sensor data 112 can also include information abouta driver of the vehicle 100, such as the driver's posture, facialexpression, heart rate, speech, movements, mental state, and/or thelike. If the vehicle 100 is a tractor and trailer, sensor data 112 aboutthe tractor and trailer state can include for example information aboutthe tractor and trailer, such as if and/or to what extent the trailer isswaying.

The memory 108 also includes (e.g., stores) a representation of anacceptable response(s) 114 for a driver of the vehicle 100. Based on adriving situation, as can be determined using the sensor data 112, thevehicle 100 and/or driver of the vehicle 100 can have some actions thatcan be acceptable (e.g., safe, desirable, not dangerous, etc.) to takegiven the driving situation, while having some other actions that maynot be acceptable (e.g., not safe, not desirable, dangerous, etc.) totake given the driving situation. The acceptable response(s) 114 canindicate for example one or more (e.g., at least two) responses that areacceptable for the vehicle 100 and/or a driver of the vehicle 100 totake given a driving scenario (as indicated by or consistent with thesensor data 112). The acceptable response(s) 114 can includerepresentations of, for example, an action (e.g., decelerating,accelerating, turning, turning off the radio, closing a window, turningheadlights on, turning windshield wipers on, etc.), a sequence ofactions (e.g., turn on blinker before changing lanes), a time rangewithin which an action should be performed, a time range within which anaction should be refrained from being performed, a time range withinwhich a state should be maintained, a distance range relative to anobstacle (e.g., nearby vehicle) where an action should be performed, adistance range relative to an obstacle where an action should berefrained from being performed, a distance range relative to an obstaclethat should be maintained, a combination thereof, and/or the like, suchas slowing down to less than a predetermined speed limit, speeding up togreater than a predetermined speed limit, switching lanes, changing amode of operation of the vehicle 100, maintaining a mode of operation ofthe vehicle 100, maintaining a speed, maintaining a distance, perform adriving maneuver, refraining from performing a driving maneuver, acombination thereof, and/or the like.

Optionally, the acceptable response(s) 114 can indicate an acceptablestate of a driver of the vehicle 100 at a given environment. Forexample, if the surrounding environment includes many obstacles, theacceptable response(s) 114 may indicate that the driver should be morealert in contrast to a scenario where the surrounding environmentincludes less obstacles. Examples of acceptable states can include beingalert, not angry, not drowsy, not distracted, etc. (as indicated byand/or inferred from sensor data that captured a facial expressionand/or body posture of a driver). Additional information related todetecting, analyzing, and/or using driver state information can be foundat U.S. Pat. No. 11,130,497, the content of which is incorporated byreference in its entirety herein.

The memory also includes (e.g., stores) a representation of CAN data116. The vehicle 100 can include a plurality of electronic control units(ECUs), such as an engine control module (ECM), a powertrain controlmodule (PCM), a transmission control module (TCM), a brake controlmodule (BCM), a central control module (CCM), a central timing module(CTM), a general electronic module (GEM), a body control module (BCM), asuspension control module (SCM), and/or the like. The CAN data 116 caninclude for example representations of communications between theplurality of ECUs. In some implementations, the CAN data 116 can includefor example information about a state of the vehicle 100, such as thevehicle's 100 speed, location, tire pressure, internal temperature,audio being played, lights that are on, windshield wiper setting, windowsettings, tractor and trailer state, and/or the like. Additionally, theCAN data 116 can include information of a change of state of the vehicle100, such as a change in the vehicle's 100 speed, a change in thevehicle's 100 location, a change in the vehicle's 100 tire pressure, achange in the vehicle's 100 internal temperature, a change in audiobeing played by the vehicle 100, a change in light settings of thevehicle 100, a change in windshield wiper settings of the vehicle 100, achange in window settings of the vehicle 100, a change in tractor andtrailer state of the vehicle 100, and/or the like.

The memory 108 also includes (e.g., stores) a representation of forexample an actual response 118 of the vehicle 100, a different vehicle(not shown in FIG. 1 ), a driver of the vehicle 100, and/or a driver ofthe different vehicle at the same scenario/environment for which theacceptable response(s) 114 was determined and/or a substantially samescenario/environment (e.g., sensor data of both scenarios/environmentsare at least 50% similar, at least 75% similar, at least 85% similar, atleast 95% similar, at least 98% similar, at least 99% similar, etc.) forwhich the acceptable response(s) 114 was determined. The actual response118 can be an actual response(s) at a time that is at substantially(e.g., within 0.1 seconds of, within 0.5 second of, within 1 second of,within 2 seconds of, within 5 seconds of, etc.) the same time that theacceptable response(s) 114 was determined, a time that is after theacceptable response(s) 114 was determined (e.g., 2 days later, 2 weekslater, 2 months later, etc.), and/or at time that is before theacceptable response(s) 114 is determined. The actual response 118 can bedetermined based on the CAN data 116 and sensor data 112. Additionallyor alternatively, the actual response 118 can be determined based on theCAN data 116, but not the sensor data 112. The actual response 118 canindicate, for example, an action, a sequence of actions, time an actionwas performed, time an action was refrained from being performed, time astate was maintained, a distance relative to an obstacle when an actionwas performed, a distance relative to an obstacle when an action wasrefrained from being performed, a distance relative to an obstacle thatwas maintained, and/or the like, such as slowing down to less than apredetermined speed limit, speeding up to greater than a predeterminedspeed limit, switching lanes, changing a mode of operation of thevehicle 100, maintaining a mode of operation of the vehicle 100,maintaining a speed, maintaining a distance, perform a driving maneuver,refrain from performing a driving maneuver, and/or the like.

Optionally, the actual response 118 can indicate a state of a driver ofthe vehicle 100 and/or a driver of another vehicle different thatvehicle 100 at a given environment. In some implementations, sensors 106located inside the vehicle can capture data on the driver to determinethe driver's state. For example, the actual response 118 can be that thedriver is alert, not angry, not drowsy, not distracted, etc.

The memory 108 also includes (e.g., stores) a representation of a driverprofile 120. The driver profile 120 can include information associatedwith the driver of the vehicle 100, such the driver's habits, height,weight, average heart rate, age, vision, health concerns, drivingrecord, years of overall driving experience, years of commercial motorvehicle driving experience, and/or the like. In some implementations,the driver profile 120 can be used, at least partially, to determine theacceptable response(s) 114. For example, if a driver of the vehicle 100is an elderly person with a poor reaction time, the vehicle 100 may needto start decelerating at a distance from an obstacle that would be lessif the driver has better vision. As another example, if a driver of thevehicle 100 has less than a week of driving experience, the vehicle 100may need to maintain a distance relative to another vehicle whenchanging lanes that would be less if the driver had more drivingexperience.

The memory 108 also includes (e.g., stores) a software model 110. Thesoftware model 110 can be, for example, an artificial intelligence (AI)model, a machine learning (ML) model, an analytical model, amathematical model or any combination thereof. The software model 110can be used (e.g., by processor 102) to determine the acceptableresponse(s) 114 for a driver of the vehicle 100 based on the sensor data112 and/or driver profile 120. For example, the sensor data 112 canindicate that the vehicle 100 is surrounded by many obstacles, in whichcase the acceptable response(s) 114 is stricter relative to a scenariowhere less obstacles are present. As another example, the sensor data112 can indicate that the vehicle 100 is not surrounded by manyobstacles, in which case the acceptable response(s) 114 is less strictrelative to a scenario where more obstacles are present. In someimplementations, the acceptable response(s) 114 is more strict as thesurrounding environment is more risky/dangerous (e.g., more obstacles,narrower roads, curvier roads, slippery roads, etc.).

The software model 110 can also be used to analyze the CAN data 116 (andoptionally, the sensor data 112) to determine the actual response 118 ofthe driver of the vehicle 100. For example, the CAN data 116 be analyzedto determine when and/or to what extent the brake pad has been pushed,indicating that the driver of the vehicle 100 has started decelerating.As another example, the CAN data 116 can be analyzed to determine when,to what direction, and/or to what extent a steering wheel of the vehicle100 has rotated, indicating that the driver of the vehicle 100 isperforming a driver maneuver to change the direction of the vehicle.

The software model 110 can also be used to compare the acceptableresponse(s) 114 to the actual response 118 and determine if a remedialaction should occur. In some implementations, the software model 110 candetermine that a remedial action should occur if the actual response 118is different than and/or substantially different (e.g., more than 5%different, more than 10% different, more than 20% different, more than33% different, more than 50% different, etc.) than the acceptableresponse(s) 114. For example, for given situation, the acceptableresponse(s) 114 may be for a driver of the vehicle 100 to begindecelerating the vehicle 100 within 125 to 175 feet from a differentvehicle in front of vehicle 100; if the actual response 118 is that thedriver of the vehicle 100 has begun decelerating the vehicle at adistance less than 125 feet or greater than 175 feet from the othervehicle in front of vehicle 100, the software model 110 can determinethat a remedial action should occur.

In some implementations, the remedial action is to change a mode ofoperation of the vehicle 100. For example, if the vehicle 100 isoperating in a manual mode, the remedial action can be to change thevehicle 100 to operate in a semi-autonomous or fully-autonomous mode(e.g., if the driver is having a health-related event such as a heartattack). As another example, if the vehicle 100 is operating intwo-wheel drive, the remedial action can be to change the vehicle 100 tooperate in four-wheel drive. As another example, if the vehicle 100 isoperating with the radio on, the remedial action can be to turn theradio off or decrease a volume of the radio (e.g., to help the driverconcentrate more in a risky/dangerous situation). As another example, ifthe vehicle 100 is operating in an eco-mode (e.g., sacrifice some powerand/or responsiveness of the vehicle 100 to get better fuel economy),the remedial action can be to change the vehicle 100 to operate in asports mode (e.g., don't sacrifice some power and/or responsiveness ofthe vehicle 100 to get better fuel economy).

In some implementations, the remedial action is to send an alert to thedriver of the vehicle 100. For example, the alert could indicate anaction(s) that the driver should take (e.g., drive less aggressively,rest, decelerate less abruptly, begin braking earlier, etc.), that thedriver did not perform an acceptable response, and/or the like. Thealert could be delivered audibly, visually, and/or the like.

In some implementations, the remedial action is to indicate the driver'sfailure to perform the acceptable response(s) 114 at the driver profile120. If, for example, the driver has failed to perform an acceptableresponse more than a predetermined number of times, the driver profile120 may be updated to indicate that the driver should take additionaltraining.

Note that, although vehicle 100 was discussed with respect to acceptableresponse(s) 114 and whether or not the actual response 118 wasacceptable, additionally or alternatively, unacceptable responses can bedetermined based on the sensor data 112. If, for example, the actualresponse 118 is an unacceptable response, a remedial action can occur.

The compute device 170 includes a processor 172 operatively coupled to amemory 174 (e.g., via a system bus). Although not shown in FIG. 1 , thecompute device 170 can be coupled to one or more other vehicles inaddition to vehicle 100 (also referred to herein as a “fleet”). Thecompute device 170 can be remote from the fleet, allowing data to bereceived from and sent to multiple different vehicles within the fleet.

The processor 172 can be, for example, a hardware based integratedcircuit (IC) or any other suitable processing device configured to runand/or execute a set of instructions or code. For example, the processor172 can be a general-purpose processor, a central processing unit (CPU),an accelerated processing unit (APU), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA), a programmablelogic array (PLA), a complex programmable logic device (CPLD), aprogrammable logic controller (PLC) and/or the like. In someimplementations, the processor 172 can be configured to run any of themethods and/or portions of methods discussed herein.

The memory 174 can be, for example, a random-access memory (RAM), amemory buffer, a hard drive, a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM), and/or the like. In someinstances, the memory 174 can store, for example, one or more softwareprograms and/or code that can include instructions to cause theprocessor 172 to perform one or more processes, functions, and/or thelike. In some embodiments, the memory 174 can include extendable storageunits that can be added and used incrementally. In some implementations,the memory 174 can be a portable memory (e.g., a flash drive, a portablehard disk, and/or the like) that can be operatively coupled to theprocessor 172. In some instances, the memory 174 can be remotelyoperatively coupled with a compute device (not shown); for example, aremote database device can serve as a memory and be operatively coupledto the compute device.

The memory 174 can also include a software model 176. The software model176 can be, for example, an artificial intelligence (AI) model, amachine learning (ML) model, an analytical model, a mathematical model,or a combination thereof. As the compute device 170 receives data (e.g.,sensor data 112, acceptable response(s) 114, actual response 118, driverprofile 120) from a fleet of vehicles, including vehicle 100, thesoftware model 176 can analyze the data to identify unwanted responses,abnormal responses, drivers that can benefit from additional training,driving characteristics of the fleet, and/or the like. Additionally,upon analysis of the data, the compute device 170 can send signals toone or more vehicles in the fleet and/or other compute devices not shownin FIG. 1 including a software update, a presentation of an indicationthat a driver is behaving atypical compared to other drivers, anindication that a driver needs additional training, and/or the like. Insome implementations, the software model 176 is an anomaly detectionmodel trained to identify responses of drivers that are anomalous. Insome implementations, the software model 176 is used for example as anassessment and/or standardized testing tool to driver training purposes,such as determining if a driver behaves acceptably and/or notanomalously.

In an example scenario with reference to FIG. 1 , vehicle 100 istravelling on a road. The sensors 106 collect sensor data 112 thatindicates that the vehicle 100 is surrounded by other vehicles. Thedriver profile 120 indicates that the driver is fairly novice. Based onthe sensor data 112 and/or driver profile 120, the software model 110determines that the vehicle 100 should not drive greater than 15 milesper an hour over the speed limit. The CAN data 116 can indicate thespeed of the vehicle 100. The software model 110 can use the CAN data116, and not the sensor data 112, to determine the actual speed of thevehicle (e.g., actual response 118). If the actual response 118indicates that vehicle 100 travelled greater than 15 miles per an hourover the speed limit, a remedial action can occur.

In another example scenario with reference to FIG. 1 , vehicle 100 istravelling on a road. The sensors 106 collect sensor data 112 thatindicates that the vehicle 100 is surrounded by other vehicles. Thedriver profile 120 indicates that the driver is somewhat experienced.Based on the sensor data 112 and/or driver profile 120, the softwaremodel 110 determines that the vehicle 100 should never decelerate at arate greater than 100 feet per second squared. The CAN data 116 canindicate if and to what extent the vehicle's 100 brake pad has beenpressed. The software model 110 can use the CAN data 116, and not thesensor data 112, to determine the deceleration rate of the vehicle(e.g., actual response 118). If the actual response 118 indicates thatthat vehicle 100 decelerated at a rate greater than 100 feet per secondsquared, a remedial action can occur.

In another example scenario with reference to FIG. 1 , vehicle 100 istravelling in a road. The sensors 106 collect sensor data 112 thatindicates that the vehicle 100 is located in front of a second vehicle,and in a lane directly to the left of the second vehicle. Based on thesensor data 112 and/or driver profile 120, the software model 110determines that the vehicle 100 should maintain a distance greater than20 feet when changing into the same lane as the second vehicle (e.g.,acceptable response(s) 114). The CAN data 116 can indicate when and towhat extent the steering wheel has turned right, as well as when thesteering wheel recentered after being turned right. The software model110 can determine a distance between the vehicle 100 and the secondvehicle as the vehicle 100 changes into the same lane as the secondvehicle (e.g., actual response 118) using (1) the CAN data 116 todetermine that the vehicle 100 is changing lanes, and (2) the sensordata 112 to determine a distance between the vehicle 100 and the secondvehicle. If the actual response 118 indicates that that vehicle 100changed lanes without maintaining a distance greater than 20 feet, aremedial action can occur.

In another example scenario with reference to FIG. 1 , the vehicle 100includes a tractor and trailer. The sensors 106 collect sensor data 112that indicates that the trailer of the vehicle 100 is fishtailing. Thesoftware model 110 determines that the vehicle 100 should deceleratequickly and perform a sharp turn to the right (e.g., acceptableresponse(s) 114). The CAN data 116 can indicate if and to what extentthe vehicle's 100 brake pad has been pressed, as well as if and to whatextent the vehicle's 100 steering wheel has been turned to the right.The software model 110 can use the CAN data 116, and not the sensor data112, to determine if the vehicle 100 decelerated quickly and performed asharp turn to the right (e.g., actual response 118). If the actualresponse 118 indicates that that vehicle 100 decelerated quickly andperformed a sharp turn to the right, a remedial action is not caused tooccur.

FIG. 2 shows a flowchart of a method 200 for causing a remedial actionto be performed in response to determining that an actual response timeis not within an acceptable response time range for a given scenario,according to an embodiment. In some implementations, method 200 can beperformed by processor 102 of the vehicle 100.

At 201, sensor data (e.g., sensor data 112) indicating a substantially360 degree surrounding of a vehicle (e.g., vehicle 100) is received viaa processor (e.g., processor 102) included in the vehicle. The sensordata is collected using a plurality of sensors (e.g., sensors 106)included in the vehicle. The sensor data can indicate the substantially360 degree surrounding of the vehicle at a given time, or across a rangeof time. In some implementations, a first subset of sensor dataassociated with a first region of the 360 degree surrounding is capturedusing a first sensor from the plurality of sensors being a first type,and a second subset of sensor data associated with a second region ofthe 360 degree surrounding different than the first region is capturedusing a second sensor from the plurality of sensors being a second typedifferent than the first type. For example, sensor data indicating afront and back region of the vehicle can be collected using a camera,and sensor data indicating a left and right region of the vehicle can becollected using a radar.

At 202, an acceptable response time range (e.g., acceptable response(s)114) for a driver of the vehicle to perform an action with the vehicleis obtained, via the processor, based on the sensor data. In someimplementations, the acceptable response time range can be determinedusing software model (e.g., software model 110). The action could be,for example, a driving maneuver, a sequence of driving maneuvers,changing a setting of the vehicle, etc. In some implementations, 202 isperformed automatically (e.g., without requiring input from a human) inresponse to completing 201.

At 203, an actual response time (e.g., actual response 118) for thedriver to perform the action is determined, via the processor, based oncontrol area network (CAN) data (e.g., CAN data 116) collected from aCAN bus included in the vehicle, and not based on the sensor data. Insome implementations, the actual response time can be determined usingsoftware model (e.g., software model 110). In some implementations, 203is performed automatically (e.g., without requiring input from a human)in response to completing 202.

At 204, a determination is made, via the processor, that the actualresponse time is not within the acceptable response time range. In someimplementations, the determination can be made using software model(e.g., software model 110). In some implementations, 204 is performedautomatically (e.g., without requiring input from a human) in responseto completing 203.

At 205, a remedial action is caused, via the processor, to be performedin response to the determining that the actual response time is notwithin the acceptable response time range. In some implementations, theremedial action is caused by the processor sending a signal(s) to thevehicle and/or a different compute device (e.g., compute device 170),where the vehicle and/or different compute device is configured toperform the remedial action in response to receiving the signal(s). Insome implementations, 205 is performed automatically (e.g., withoutrequiring input from a human) in response to completing 204.

In some implementations of method 200, the acceptable response timerange for the driver to perform the remedial action is further based ona driver profile (e.g., driver profile 120) associated with the driver.

In some implementations of method 200, the vehicle is a tractor attachedto a trailer. In some implementations of method 200, the acceptableresponse time range for the driver to perform the remedial action isfurther based on a state of the tractor and the trailer.

In some implementations of method 200, the vehicle is a semi-autonomousheavy truck. For example, the vehicle may operate at one or more oflevel two autonomy, level three autonomy, or level four autonomy.

In some implementations of method 200, the remedial action is todecrease a speed of the vehicle to less than a predetermined speedthreshold (e.g., speed limit associated with the vehicle's location, atleast 5% less than the speed limit associated with the vehicle'slocation, at least 10% less than the speed limit associated with thevehicle's location, at least 25% less than the speed limit associatedwith the vehicle's location, 20 miles per hour, 15 miles per hour, 10miles per hour, 5 miles per hour, 1 mile per hour, etc.). In someimplementations of method 200, the remedial action is to change a modeof operation of the vehicle. In some implementations of method 200, theremedial action is to recommend that the driver receive additionaldriver training.

Some implementations of method 200 further include causing state data ofthe driver to be collected, via the processor, in response to thedetermining that the actual response time is not within the acceptableresponse time range at 204. State data could be collected by at leastone sensor from the plurality of sensors. The state data could indicatea state of the driver, such as the driver's mental state (e.g., angry,drowsy, etc.) or physical state (e.g., slouched, looking downwards,etc.). Some implementations of method 200 further include updating, viathe processor, a driver profile associated with the driver based on thestate data of the driver.

In some implementations of method 200, the plurality of sensors includesa camera, a radar, and a lidar. In some implementations of method 200,the plurality of sensors includes a plurality of cameras, a plurality ofradars, and a plurality of lidars. In some implementations, theplurality of sensors includes only cameras, only radars, and/or onlylidars.

FIG. 3 shows a flowchart of a method 300 for comparing an acceptableresponse and actual response at each environment from a set ofenvironments, according to an embodiment. In some implementations,method 300 can be performed by processor 102 of the vehicle 100.

At 301, a first set of sensor data (e.g., sensor data 112) indicating asurrounding of a vehicle (e.g., vehicle 100) at a first environment isreceived via a processor (e.g., processor 102) included in the vehicle.The first set of sensor data is collected using a plurality of sensors(e.g., sensors 106) included in the vehicle.

At 302, a first set of acceptable responses (e.g., acceptableresponse(s) 114) for a driver of the vehicle is determined, via theprocessor, based on the first set of sensor data. The first set ofacceptable responses can include one or more acceptable responses (e.g.,at least one, at least two, etc.). In some implementations, the firstset of acceptable responses can be determined using software model(e.g., software model 110). In some implementations, 302 is performedautomatically (e.g., without requiring input from a human) in responseto completing 301.

At 303, a first actual response (e.g., actual response 118) of thedriver is determined, via the processor, based on a first set of controlarea network (CAN) data (e.g., CAN data 116) collected from a CAN busincluded in the vehicle. In some implementations, the first actualresponse is further determined based on sensor data (e.g., sensor data112). In some implementations, the first actual response is notdetermined based on sensor data (e.g., sensor data 112). In someimplementations, the first actual response can be determined usingsoftware model (e.g., software model 110). In some implementations, 303is performed automatically (e.g., without requiring input from a human)in response to completing 302.

At 304, a determination is made, via the processor, that the firstactual response is not included in the first set of acceptableresponses. In some implementations, the determination is made that thefirst actual response is not included in the first set of acceptableresponses using software model 110. In some implementations, 304 isperformed automatically (e.g., without requiring input from a human) inresponse to completing 303.

At 305, a remedial action is caused, via the processor, to be performedin response to the determining that the first actual response is notincluded in the first set of acceptable responses. In someimplementations, the remedial action is caused by the processor sendinga signal(s) to the vehicle and/or a different compute device (e.g.,compute device 170), where the vehicle and/or different compute deviceis configured to perform the remedial action in response to receivingthe signal(s). In some implementations, 305 is performed automatically(e.g., without requiring input from a human) in response to completing304.

At 306, a second set of sensor data different than the first set ofsensor data and indicating a surrounding of the vehicle at a secondenvironment different than the first environment is received via theprocessor. The second set of sensor data is collected using theplurality of sensors included in the vehicle. The second environment maydiffer from the first environment based one or more attributes, such aslocation of obstacles, size of obstacles, speed of obstacles, weatherconditions, location, and/or the like.

At 307, a second set of acceptable responses different than the firstset of acceptable responses for the driver is determined, via theprocessor, based on the second set of sensor data. In someimplementations, the second set of acceptable responses can bedetermined using software model (e.g., software model 110). In someimplementations, 307 is performed automatically (e.g., without requiringinput from a human) in response to completing 306.

At 308, a second actual response of the driver is determined, via theprocessor, based on a second set of CAN data collected from the CAN busincluded in the vehicle. In some implementations, the second actualresponse of the driver is determined using software model (e.g.,software model 110). In some implementations, 308 is performedautomatically (e.g., without requiring input from a human) in responseto completing 307.

At 309, a determination is made, via the processor, that the secondactual response is included in the second set of acceptable responses.In some implementations, the determination is made that the secondactual response is included in the second set of acceptable responsesusing software model (e.g., software model 110). In someimplementations, 309 is performed automatically (e.g., without requiringinput from a human) in response to completing 308.

In some implementations of method 300, the vehicle is a tractor attachedto a trailer. In some implementations of method 300, the first set ofsensor data is collected at a first time, and the first set ofacceptable responses are further based on a first state of the tractorand the trailer at the first time. In some implementations of method300, the second set of sensor data is collected at a second time, andthe second set of acceptable responses are further based on a secondstate of the tractor and the trailer at the second time.

Some implementations of method 300 further include causing, via theprocessor, a machine learning model (e.g., software model 176) includedin a compute device (e.g., compute device 170) remote from the processorto be trained based on at least one of the representation of the firstset of sensor data, the first actual response, the second set of sensordata, or the second actual response.

Some implementations of method 300 further include receiving, via theprocessor, a third set of sensor data different than the first set ofsensor data and the second set of sensor data. The third set of sensordata can indicate a surrounding of the vehicle at a third environment.The third set of sensor data can be collected using the plurality ofsensors included in the vehicle. Some implementations of method 300 canfurther include determining, via the processor, a third set ofacceptable responses for the driver based on the third set of sensordata. Some implementations of method 300 can further includedetermining, via the processor, a third actual response of the driverbased on a third set of CAN data collected from the CAN bus included inthe vehicle. Some implementations of method 300 further includereceiving, via the processor, a fourth set of sensor data different thanthe first set of sensor data, the second set of sensor data, and thethird set of sensor data. The further set of sensor data can indicate asurrounding of the vehicle at a fourth environment. The fourth set ofsensor data can be collected using the plurality of sensors included inthe vehicle. Some implementations of method 300 further includedetermining, via the processor, a fourth set of acceptable responses forthe driver based on the fourth set of sensor data. Some implementationsof method 300 further include determining, via the processor, a fourthactual response of the driver based on a fourth set of CAN datacollected from the CAN bus included in the vehicle. Note that, whilefour sets of sensor data and four sets of acceptable responses arediscussed, it should be understood that there can be more than four inthe course of a driver driving a vehicle.

In some implementations of method 300, the plurality of sensors includesa camera, a radar, and a lidar. In some implementations of method 300,the plurality of sensors includes a plurality of cameras, a plurality ofradars, and a plurality of lidars. In some implementations of method300, the plurality of sensors includes only cameras, only radars, and/oronly lidars. In some implementations, any combination of cameras,radars, and/or lidars can be used (e.g., two cameras and one lidar, twolidars and eight cameras, etc.)

In some implementations of method 300, the first environment includes afirst number of obstacles within a predetermined radius of the vehicle(e.g., within 1 meter, within 5 meters, within 10 meters, within 20meters, etc.), the first set of acceptable responses includes a firstacceptable reaction time limit, the second environment includes a secondnumber of obstacles within the predetermined radius of the vehicle, thesecond number is less than the first number, the second set ofacceptable responses is a second acceptable reaction time limit, and thesecond acceptable reaction time limit is greater than the firstacceptable reaction time limit.

FIG. 4 shows a flowchart of a method 400 for determining that a responseof a driver is anomalous, according to an embodiment. In someimplementations, method 400 can be performed by the processor 172 ofcompute device 170.

At 401, a first training dataset is received. The first training datasetcan include (1) a first set of sensor data (e.g., sensor data 112)indicating a surrounding of a first vehicle (e.g., vehicle 100), and (2)a representation of a response (e.g., actual response 118) of a driverof the first vehicle to perform an action based on a set of control areanetwork (CAN) data (e.g., CAN data 116) collected from a CAN busincluded in the first vehicle.

At 402, a second training dataset is received. The second training datacan include (1) a second set of sensor data indicating a surrounding ofa second vehicle, and (2) a representation of a response of a driver ofthe second vehicle to perform the action based on a set of CAN datacollected from a CAN bus included in the second vehicle. In someimplementations, the second vehicle is different than the first vehicle.In some implementations, the second vehicle is the same as the firstvehicle. In some implementations, the surrounding of the first vehicleis different than the surrounding of the second vehicle. In someimplementations, the surrounding of the first vehicle is the same as thesurrounding of the second vehicle. In some implementations, 402 can beperformed prior to 401. In some implementations, 402 and 401 areperformed in parallel.

At 403, an anomaly detection model is trained, using the first trainingdataset and the second training dataset, to produce a trained anomalydetection model (e.g., software model 176). In some implementations, theanomaly detection model is trained to identify responses that areatypical/not baseline/abnormal for a given surrounding. The anomalydetection model can be trained using semi-supervised learning orunsupervised learning. In some implementations, training the anomalydetection model using the CAN data from the first training dataset andthe CAN data from the second training dataset produces betterpredictions/identifications of anomalous behavior (e.g., faster, moreaccurate, more comprehensive, etc.) than would be the case for theanomaly detection model being trained with just the first set of sensordata and the second set of sensor data.

At 404, (1) a third set of sensor data indicating a surrounding of athird vehicle, and (2) a representation of a response of a driver of thethird vehicle to perform the action based on a set of CAN data collectedfrom a CAN bus included in the third vehicle are received. In someimplementations, the third vehicle is different than the first vehicleand/or the second vehicle. In some implementations, the third vehicle isthe same as the first vehicle and/or the second vehicle. In someimplementations, the surrounding of the third vehicle is different thanthe surrounding of the first vehicle and/or the surrounding of thesecond vehicle. In some implementations, the surrounding of the thirdvehicle is the same as the surrounding of the first vehicle and/or thesurrounding of the second vehicle.

At 405, a determination is made that the response of the driver of thethird vehicle is anomalous using the trained anomaly detection model,the third set of sensor data, and the representation of the response ofthe driver of the third vehicle. In some implementations, 405 isperformed automatically (e.g., without requiring input from a human) inresponse to completing 404.

At 406, a remedial action is caused to be performed in response todetermining that the response of the driver of the third vehicleanomalous. In some implementations, the remedial action is caused by theprocessor sending a signal(s) to the third vehicle and/or a differentcompute device, where the third vehicle and/or different compute deviceis configured to perform the remedial action in response to receivingthe signal(s). In some implementations, 406 is performed automatically(e.g., without requiring input from a human) in response to completing405.

In some implementations of method 400, the first vehicle is a firstsemi-truck, the second vehicle is a second semi-truck, and the thirdvehicle is a third semi-truck.

Some implementations of method 400 further include updating the trainedanomaly detection model using the third set of sensor data and therepresentation of the response of the driver of the third vehicle togenerate an updated trained anomaly detection model. Someimplementations of method 400 further include receiving (1) a fourth setof sensor data indicating a surrounding of a fourth vehicle, and (2) arepresentation of a response of a driver of the fourth vehicle toperform the action based on a set of CAN data collected from a CAN busincluded in the fourth vehicle. Some implementations of method 400further include inputting the fourth set of sensor data and therepresentation of the response of the driver of the fourth vehicle tothe updated trained anomaly detection model.

Some implementations of method 400 further include receiving (1) afourth set of sensor data indicating a surrounding of a fourth vehicle,and (2) a representation of a response of a driver of the fourth vehicleto perform the action based on a set of CAN data collected from a CANbus included in the fourth vehicle. Some implementations of method 400further include determining that the fourth response is not anomaloususing the trained anomaly detection model, the fourth set of sensordata, and the representation of the response of the driver of the fourthvehicle. Some implementations of method 400 further include refrainingfrom causing a remedial action to be performed in response todetermining that the response of the driver of the fourth vehicle is notanomalous.

It should be understood that the disclosed embodiments are not intendedto be exhaustive, and functional, logical, operational, organizational,structural and/or topological modifications may be made withoutdeparting from the scope of the disclosure. As such, all examples and/orembodiments are deemed to be non-limiting throughout this disclosure.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

Examples of computer code include, but are not limited to, micro-code ormicro-instructions, machine instructions, such as produced by acompiler, code used to produce a web service, and files containinghigher-level instructions that are executed by a computer using aninterpreter. For example, embodiments can be implemented using Python,Java, JavaScript, C++, and/or other programming languages anddevelopment tools. Additional examples of computer code include, but arenot limited to, control signals, encrypted code, and compressed code.

The drawings primarily are for illustrative purposes and are notintended to limit the scope of the subject matter described herein. Thedrawings are not necessarily to scale; in some instances, variousaspects of the subject matter disclosed herein can be shown exaggeratedor enlarged in the drawings to facilitate an understanding of differentfeatures. In the drawings, like reference characters generally refer tolike features (e.g., functionally similar and/or structurally similarelements).

The acts performed as part of a disclosed method(s) can be ordered inany suitable way. Accordingly, embodiments can be constructed in whichprocesses or steps are executed in an order different than illustrated,which can include performing some steps or processes simultaneously,even though shown as sequential acts in illustrative embodiments. Putdifferently, it is to be understood that such features may notnecessarily be limited to a particular order of execution, but rather,any number of threads, processes, services, servers, and/or the likethat may execute serially, asynchronously, concurrently, in parallel,simultaneously, synchronously, and/or the like in a manner consistentwith the disclosure. As such, some of these features may be mutuallycontradictory, in that they cannot be simultaneously present in a singleembodiment. Similarly, some features are applicable to one aspect of theinnovations, and inapplicable to others.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range is encompassed within the disclosure. That the upper andlower limits of these smaller ranges can independently be included inthe smaller ranges is also encompassed within the disclosure, subject toany specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the disclosure.

The phrase “and/or,” as used herein in the specification and in theembodiments, should be understood to mean “either or both” of theelements so conjoined, i.e., elements that are conjunctively present insome cases and disjunctively present in other cases. Multiple elementslisted with “and/or” should be construed in the same fashion, i.e., “oneor more” of the elements so conjoined. Other elements can optionally bepresent other than the elements specifically identified by the “and/or”clause, whether related or unrelated to those elements specificallyidentified. Thus, as a non-limiting example, a reference to “A and/orB”, when used in conjunction with open-ended language such as“comprising” can refer, in one embodiment, to A only (optionallyincluding elements other than B); in another embodiment, to B only(optionally including elements other than A); in yet another embodiment,to both A and B (optionally including other elements); etc.

As used herein in the specification and in the embodiments, “or” shouldbe understood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the embodiments, “consisting of,” will refer to the inclusion ofexactly one element of a number or list of elements. In general, theterm “or” as used herein shall only be interpreted as indicatingexclusive alternatives (i.e., “one or the other but not both”) whenpreceded by terms of exclusivity, such as “either,” “one of,” “only oneof,” or “exactly one of.” “Consisting essentially of,” when used in theembodiments, shall have its ordinary meaning as used in the field ofpatent law.

As used herein in the specification and in the embodiments, the phrase“at least one,” in reference to a list of one or more elements, shouldbe understood to mean at least one element selected from any one or moreof the elements in the list of elements, but not necessarily includingat least one of each and every element specifically listed within thelist of elements and not excluding any combinations of elements in thelist of elements. This definition also allows that elements canoptionally be present other than the elements specifically identifiedwithin the list of elements to which the phrase “at least one” refers,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, “at least one of A and B” (or,equivalently, “at least one of A or B,” or, equivalently “at least oneof A and/or B”) can refer, in one embodiment, to at least one,optionally including more than one, A, with no B present (and optionallyincluding elements other than B); in another embodiment, to at leastone, optionally including more than one, B, with no A present (andoptionally including elements other than A); in yet another embodiment,to at least one, optionally including more than one, A, and at leastone, optionally including more than one, B (and optionally includingother elements); etc.

In the embodiments, as well as in the specification above, alltransitional phrases such as “comprising,” “including,” “carrying,”“having,” “containing,” “involving,” “holding,” “composed of,” and thelike are to be understood to be open-ended, i.e., to mean including butnot limited to. Only the transitional phrases “consisting of” and“consisting essentially of” shall be closed or semi-closed transitionalphrases, respectively, as set forth in the United States Patent OfficeManual of Patent Examining Procedures, Section 2111.03.

Some embodiments described herein relate to a computer storage productwith a non-transitory computer-readable medium (also can be referred toas a non-transitory processor-readable medium) having instructions orcomputer code thereon for performing various computer-implementedoperations. The computer-readable medium (or processor-readable medium)is non-transitory in the sense that it does not include transitorypropagating signals per se (e.g., a propagating electromagnetic wavecarrying information on a transmission medium such as space or a cable).The media and computer code (also can be referred to as code) can bethose designed and constructed for the specific purpose or purposes.Examples of non-transitory computer-readable media include, but are notlimited to, magnetic storage media such as hard disks, floppy disks, andmagnetic tape; optical storage media such as Compact Disc/Digital VideoDiscs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), andholographic devices; magneto-optical storage media such as opticaldisks; carrier wave signal processing modules; and hardware devices thatare specially configured to store and execute program code, such asApplication-Specific Integrated Circuits (ASICs), Programmable LogicDevices (PLDs), Read-Only Memory (ROM) and Random-Access Memory (RAM)devices. Other embodiments described herein relate to a computer programproduct, which can include, for example, the instructions and/orcomputer code discussed herein.

Some embodiments and/or methods described herein can be performed bysoftware (executed on hardware), hardware, or a combination thereof.Hardware modules may include, for example, a processor, a fieldprogrammable gate array (FPGA), and/or an application specificintegrated circuit (ASIC). Software modules (executed on hardware) caninclude instructions stored in a memory that is operably coupled to aprocessor, and can be expressed in a variety of software languages(e.g., computer code), including C, C++, Java™ Ruby, Visual Basic™,and/or other object-oriented, procedural, or other programming languageand development tools. Examples of computer code include, but are notlimited to, micro-code or micro-instructions, machine instructions, suchas produced by a compiler, code used to produce a web service, and filescontaining higher-level instructions that are executed by a computerusing an interpreter. For example, embodiments may be implemented usingimperative programming languages (e.g., C, Fortran, etc.), functionalprogramming languages (Haskell, Erlang, etc.), logical programminglanguages (e.g., Prolog), object-oriented programming languages (e.g.,Java, C++, etc.) or other suitable programming languages and/ordevelopment tools. Additional examples of computer code include, but arenot limited to, control signals, encrypted code, and compressed code.

What is claimed is:
 1. A computer-implemented method comprising:providing, by a computing system, training data for a machine learningmodel based on sensor data of a plurality of vehicles and responses of aplurality of drivers of the plurality of vehicles; determining, by thecomputing system, that a response of a driver of a vehicle is abnormalbased on sensor data of an environment of the vehicle and the machinelearning model; and causing, by the computing system, a remedial actionto be performed based on the response of the driver.
 2. Thecomputer-implemented method of claim 1, wherein the training dataincludes a first training dataset that includes a first set of sensordata of a first vehicle and a first response of a first driver of thefirst vehicle and a second training dataset that includes a second setof sensor data of the first vehicle and a second response of a seconddriver of the first vehicle.
 3. The computer-implemented method of claim1, further comprising: providing, by the computing system, a trainingdataset that includes the sensor data of the environment of the vehicleand the response of the driver to the machine learning model; andupdating, by the computing system, the machine learning model based onthe training dataset.
 4. The computer-implemented method of claim 1,further comprising: determining, by the computing system, a speed limitbased on a driver profile associated with the driver of the vehicle,wherein the determining that the response of the driver of the vehicleis abnormal is based on the speed limit.
 5. The computer-implementedmethod of claim 1, wherein the remedial action includes a decrease of aspeed of the vehicle to less than a predetermined threshold less than aspeed limit associated with a location of the vehicle.
 6. Thecomputer-implemented method of claim 1, wherein the remedial actionincludes an alert that indicates the driver has performed an abnormalresponse and indicates an action for the driver to perform.
 7. Thecomputer-implemented method of claim 1, further comprising: determining,by the computing system, the driver has performed an abnormal responseat least a predetermined number of times; and updating, by the computingsystem, a driver profile associated with the driver based on theperformance of the abnormal response at least the predetermined numberof times.
 8. The computer-implemented method of claim 1, furthercomprising: determining, by the computing system, that a second responseof a second driver of a second vehicle is normal based on second sensordata and the machine learning model; and preventing, by the computingsystem, performance of a remedial action based on the second response ofthe second driver.
 9. The computer-implemented method of claim 1,further comprising: determining, by the computing system, the responseof the driver of the vehicle based on a control area network bus in thevehicle.
 10. The computer-implemented method of claim 1, furthercomprising: performing, by the computing system, an assessment of thedriver based on the machine learning model.
 11. A system comprising: atleast one processor; and a memory storing instructions that, whenexecuted by the at least one processor, cause the system to performoperations comprising: providing training data for a machine learningmodel based on sensor data of a plurality of vehicles and responses of aplurality of drivers of the plurality of vehicles; determining that aresponse of a driver of a vehicle is abnormal based on sensor data of anenvironment of the vehicle and the machine learning model; and causing aremedial action to be performed based on the response of the driver. 12.The system of claim 11, wherein the training data includes a firsttraining dataset that includes a first set of sensor data of a firstvehicle and a first response of a first driver of the first vehicle anda second training dataset that includes a second set of sensor data ofthe first vehicle and a second response of a second driver of the firstvehicle.
 13. The system of claim 11, the operations further comprising:providing, by the computing system, a training dataset that includes thesensor data of the environment of the vehicle and the response of thedriver to the machine learning model; and updating the machine learningmodel based on the training dataset.
 14. The system of claim 11, theoperations further comprising: determining a speed limit based on adriver profile associated with the driver of the vehicle, wherein thedetermining that the response of the driver of the vehicle is abnormalis based on the speed limit.
 15. The system of claim 11, wherein theremedial action includes a decrease of a speed of the vehicle to lessthan a predetermined threshold less than a speed limit associated with alocation of the vehicle.
 16. A non-transitory computer-readable storagemedium including instructions that, when executed by at least onprocessor of a computing system, cause the computing system to performoperations comprising: providing training data for a machine learningmodel based on sensor data of a plurality of vehicles and responses of aplurality of drivers of the plurality of vehicles; determining that aresponse of a driver of a vehicle is abnormal based on sensor data of anenvironment of the vehicle and the machine learning model; and causing aremedial action to be performed based on the response of the driver. 17.The non-transitory computer-readable storage medium of claim 16, whereinthe training data includes a first training dataset that includes afirst set of sensor data of a first vehicle and a first response of afirst driver of the first vehicle and a second training dataset thatincludes a second set of sensor data of the first vehicle and a secondresponse of a second driver of the first vehicle.
 18. The non-transitorycomputer-readable storage medium of claim 16, the operations furthercomprising: providing, by the computing system, a training dataset thatincludes the sensor data of the environment of the vehicle and theresponse of the driver to the machine learning model; and updating themachine learning model based on the training dataset.
 19. Thenon-transitory computer-readable storage medium of claim 16, theoperations further comprising: determining a speed limit based on adriver profile associated with the driver of the vehicle, wherein thedetermining that the response of the driver of the vehicle is abnormalis based on the speed limit.
 20. The non-transitory computer-readablestorage medium of claim 16, wherein the remedial action includes adecrease of a speed of the vehicle to less than a predeterminedthreshold less than a speed limit associated with a location of thevehicle.